Security Engineer / Research Engineer, RL Security

About the role

Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is a large distributed system: training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts. As an engineer in RL Engineering, you'll work on keeping that system secure as it scales.

This is a hands-on engineering role. You'll work in the RL codebase and the infrastructure underneath it, alongside the engineers and researchers who build and use it. The system is large and changes as fast as the research does, so security work here has to keep up with that pace without costing performance or velocity.

We're hiring from two backgrounds: security engineers who want to work on large-scale ML systems, and ML systems or research engineers who want to work on security. Strong candidates have depth in one and real interest in the other.

Key responsibilities

Design, build, and operate security controls for RL training systems and the infrastructure they run on

Work across the stack (orchestration, environments, training, inference) wherever the work is needed

Review designs and code across the RL stack, find security problems, and fix them

Build shared libraries and tooling that other RL engineers and researchers build on

Build the observability needed to understand what these systems are doing

Work with researchers and performance engineers to make sure security changes preserve training correctness and performance

Write clear design documents for what you build

Minimum qualifications

Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go

Experience designing, building, and operating large-scale distributed systems or security systems in production

Strong understanding of security fundamentals

Working knowledge of Linux, containers, and cloud infrastructure

Experience debugging complex problems across many hosts and services

Ability to reason about tradeoffs between security, performance, and reliability

Strong written communication, including design documents

Preferred qualifications

Experience running ML training or inference infrastructure at scale

Experience with infrastructure security, such as isolation, access control, networking, or identity

Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale

Experience building observability for large fleets

Experience supporting research teams or other fast-moving internal users

Familiarity with reinforcement learning or large language model training workloads

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$320,000—$405,000 USD

Logistics

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

How we're different

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.